Situating social connectedness in healthy cities: a conceptual primer for research and policy
Bibliographic record
Abstract
In response to growing levels of social isolation and loneliness in cities, the promotion of social connectedness has come to the forefront of urban health, sustainability, and resiliency agendas. Despite policy attention locally and internationally, social connectedness is not consistently defined, conceptualized, or measured in population health and urban planning research. The term has also been used interchangeably with various other concepts in research on social environments and health, particularly social cohesion, social capital, and social inclusion. These discrepancies create confusion for planners and policymakers looking for evidence-informed guidance on the implementation and evaluation of urban interventions designed to promote social connectedness. Further, it presents a challenge for intervention researchers interested in investigating possible causal pathways between urban change, social connectedness, and health. Drawing from contemporary public health and urban planning literature, this paper aims to delineate the concept of social connectedness, including its meaning, measurement, and relationship to neighbourhoods and health. Clarifying social connectedness for urban health research and policy is crucial to interpreting and advancing evidence on its role – both its determinants and impacts – in the development of healthy, sustainable, and resilient cities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.010 | 0.079 |
| Scholarly communication | 0.020 | 0.041 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".